State of Charge Estimation for Liquid Metal Battery Using Kalman Filter

This paper proposed an effective battery model together with a state of charge(SOC) estimation model designed specifically for Liquid Metal Battery (LMB). LMB is a potential energy storage battery very suitable for power grid, with long life-span, large charge current and high energy density. The parameters of this LMB model was determined based on the second order Thevenin circuit. The value of these parameters was calculated according to the experimental data and global optimization algorithm. Data comparison between the battery terminal voltage obtained from the experiment and calculated by model is made to prove the accuracy of the LMB model. The SOC estimation model was built using Kalman Filter algorithm and the LMB model established in this paper. The SOC estimation model performed an accurate performance without the necessity of an initial value under different dynamic conditions or static condition. Data comparison between the SOC obtained from the experiment and calculated by model is made to prove the accuracy of the SOC estimation model. The root-mean square error of these two sets of data is 0.0171.